In games, training environments, and other virtual simulations, non-player characters (virtual agents) perform actions to interact with the 3-D graphical objects surrounding them to accomplish specified goals. These interactions and other components of a virtual agent’s behavior are generally created by hand by a simulation author. Artificial Intelligence (AI) and planning systems then utilize these created actions to allow virtual agents to reason over their abilities considering their environment. However, any behavior or interaction the simulation author wishes to have the virtual agent perform must be created beforehand. When using actions that have several components, the creation process is tedious (and possibly combinatoric). This is especially true when faced with many objects and actions, when multiple objects can be used for an action. While this burden is decreased to some extent with the use of hierarchies where information can be generalized, the total number of possible connections still explodes as the number of actions and objects in a simulation grow. It is also important for an AI system to have a consistent representation of the meaning of each action. Also, a simulation author hand-crafting actions can lead to inconsistencies between actions, such as having actions that can never be completed or should, but do not, connect actions to all usable objects. To address this issue, we show how to automate the processes for describing, organizing, and generating the meaning (semantics) of actions. Specifically, we formalized and refined the Parameterized Action Representation (PAR) to better exploit its capabilities and enable actions to have their generation automated.

PAR is unique in that it allows for intelligent organization of virtual agent actions through the use of action taxonomies. From this, we have formulated algorithms and measures to determine the advantage of action organization strategies based on their end application. Furthermore, we developed and evaluated novel methods to automate the population of action taxonomies from base action names through use of existing lexical databases. Our work culminates in a transformative pipeline to automate connections between virtual agent actions and 3-D graphical models as well as the population of action semantics. By defining and refining PAR, as well as automating populating the connections made between virtual agent actions and 3-D graphical models, action sets can be transferred between different virtual environments and ease the simulation author’s burden.